Wavelet filters for automated recognition of birdsong in long‐time field recordings. Issue 3 (26th February 2020)

• Record Type: Journal Article
• Authors: Priyadarshani, Nirosha; Marsland, Stephen; Juodakis, Julius; Castro, Isabel; Listanti, Virginia
• Editors: Blomberg, Simone
• Is Part Of: Methods in ecology and evolution. Volume 11:Issue 3(2020)
• Page Start: 403
• Page End: 417
• Publication Date: 2020-02-26
• Subjects: acoustic surveys -- automated birdsong recognition -- ecoacoustics -- false negatives -- field recordings -- machine learning -- wavelet filters -- wavelets; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13357
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded – recognizing calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone – are not well‐developed yet. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimize false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel‐Frequency Cepstral Coefficients) on real‐world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hr of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large‐scale monitoring of animal species. It can be trained to recognize new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.